Machine learning for java developers
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Machine learning for java developers
- 1. Machine Learning for Java Developers Nirmal Fernando WSO2 Inc. {Java Colombo}
- 2. Few things about me... ● Associated Technical Lead at WSO2 ● Team Lead of WSO2 Machine Learner ● Just completed 4th year in the industry ● Graduated from Department of Computer Science, University of Moratuwa. ● Schooled at St. Sebastian’s College, Moratuwa. ● Can sing a bit :-) https://goo.gl/qbAXLz
- 3. Predictive Analytics Extract information from existing datasets to determine patterns and predict future outcomes and trends. It does not tell you what will happen in the future. But forecasts what might happen in the future with an acceptable level of reliability. source: http://insidebigdata.com/2014/08/25/salespredict- marketo-partner-using-predictive-analytics/
- 4. Predictive Analytics “Big Data Predictive Analytics” Forrester Research report is the second most read Forrester report in Q3, 2015 https://www.forrester.com
- 5. Predictive Analytics - Use cases http://californialoanfind.com/what-and-who-is-teletrack/
- 6. Predictive Analytics - Use cases http://www.chrisdunn.com/
- 7. Machine Learning Field of study that gives computers the ability to learn without being explicitly programmed. - Arthur Samuel (1959)
- 8. Machine Learning - Pipeline
- 9. Machine Learning - Terminology ● Input data must be in tabular format ● Each row is called a data point ● Each column is called a feature ● Value you are going to predict is called the “response variable”
- 10. ● Next value prediction ● Classification ● Clustering ● Recommendations etc… Machine Learning - What type of a problem?
- 11. Next value prediction Example of linear regression on one independent variable
- 12. Predicting a discrete value Classification
- 13. Grouping similar data points together. Clustering
- 14. Seek to predict preferences a user would give to an item/product. Recommendations
- 15. ● Supervised learning ● Unsupervised learning ● Reinforcement learning Machine Learning - Which algorithm category?
- 17. Supervised Learning Algorithms Regression Classification Linear Regression Lasso Regression Ridge Regression Logistic Regression Support Vector Machine (SVM) Decision Tree Random Forest Naive Bayes Bayesian Network
- 19. Java tools for Machine Learning Tool License URL Weka GNU General Public License http://www.cs. waikato.ac. nz/ml/weka/ JSAT GPL v3 https://github. com/EdwardRaff/JSAT Mahout Apache v2 https://mahout. apache.org/ Spark MLlib Apache v2 http://spark.apache. org/mllib/
- 20. Speed Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Ease of Use Write applications quickly in Java, Scala, Python, R. Easy to Deploy Runs on existing Hadoop clusters and data. Apache Spark MLlib - scalable machine learning library
- 21. SparkConf - Configuration for a Spark application. Used to set various Spark parameters as key-value pairs. SparkContext / JavaSparkContext - Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster. Only one SparkContext may active per JVM. RDD / JavaRDD - A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable, partitioned collection of elements that can be operated in parallel. Apache Spark - few terms
- 22. Filter - Return a new dataset formed by selecting those elements of the source on which function returns true. Map - Return a new distributed dataset formed by passing each element of the source through a function. Random Split - Split a dataset randomly based on a given ratio. Cache - Persisting (or caching) a dataset in memory across operations. Apache Spark - few operations on a RDD
- 23. ● Dataset Pima Indian diabetes dataset https://archive.ics.uci. edu/ml/datasets/Pima+Indians+Diabetes Number of instances : 768 Number of features : 8 Let’s solve a classification problem using Apache Spark
- 24. ● Response variable Name : class Values : 0 or 1 Interpretation : Whether a given Pima Indian has diabetes or not Let’s solve a classification problem using Apache Spark
- 25. ● Objective Build a classification model to predict whether a given Pima Indian has diabetes or not. Let’s try to build a Logistic Regression model for this. Let’s solve a classification problem using Apache Spark
- 27. Powered by Apache Spark and Apache Spark MLlib. ● Manage and explore your data ● Analyze the data using machine learning algorithms ● Build machine learning models ● Compare and manage generated machine learning models ● Predict using the built models ● Use the built models with WSO2 CEP and WSO2 ESB. http://wso2.com/products/machine-learner/ Few words on WSO2 Machine Learner
Notes de l'éditeur
- Fraud detection
- stock market prediction Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit.
- stock market prediction
- stock market prediction
- Reinforcement learning : A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent
- Reinforcement learning : A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent
- Mention about the row wise operations
- Reinforcement learning : A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent
- Reinforcement learning : A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent